{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-Timescale Prediction\n",
    "\n",
    "This notebook showcases some ways to use the **MTS-LSTM** from our recent publication to generate predictions at multiple timescales: [**\"Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network\"**](https://hess.copernicus.org/articles/25/2045/2021/).\n",
    "\n",
    "Let's assume we have a set of daily meteorological forcing variables and a set of hourly variables, and we want to generate daily and hourly discharge predictions.\n",
    "Now, we could just go and train two separate LSTMs: One on the daily forcings to generate daily predictions, and one on the hourly forcings to generate hourly ones. \n",
    "One problem with this approach: It takes a _lot_ of time, even if you run it on a GPU. \n",
    "The reason is that the hourly model would crunch through a years' worth of hourly data to predict a single hour (assuming we provide the model input sequences with the same look-back that we usually use with daily data).\n",
    "That's $365 \\times 24 = 8760$ time steps to process for each prediction.\n",
    "Not only does this take ages to train and evaluate, but also the training procedure becomes quite unstable and it is theoretically really hard for the model to learn dependencies over that many time steps.\n",
    "What's more, the daily and hourly predictions might end up being inconsistent, because the two models are entirely unrelated.\n",
    "\n",
    "## MTS-LSTM\n",
    "\n",
    "MTS-LSTM solves these issues: We can use a single model to predict both hourly and daily discharge, and with some tricks, we can push the model toward predictions that are consistent across timescales.\n",
    "\n",
    "### The Intuition\n",
    "\n",
    "The basic idea of MTS-LSTM is this: we can process time steps that are far in the past at lower temporal resolution.\n",
    "As an example, to predict discharge of September 10 9:00am, we'll certainly need fine-grained data for the previous few days or weeks.\n",
    "We might also need information from several months ago, but we probably _don't_ need to know if it rained at 6:00am or 7:00am on May 15.\n",
    "It's just so long ago that the fine resolution doesn't matter anymore.\n",
    "\n",
    "### How it's Implemented\n",
    "\n",
    "The MTS-LSTM architecture follows this principle: To predict today's daily and hourly dicharge, we start feeding daily meteorological information from up to a year ago into the LSTM.\n",
    "At some point, say 14 days before today, we split our processing into two branches:\n",
    "1. The first branch just keeps going with daily inputs until it outputs today's daily prediction.\n",
    "So far, there's no difference to normal daily-only prediction.\n",
    "2. The second branch is where it gets interesting: We take the LSTM state from 14 days before today, apply a linear transformation to it, and then use the resulting states as the starting point for another LSTM, which we feed the 14 days of _hourly_ data until it generates today's 24 hourly predictions.\n",
    "\n",
    "Thus, in a single forward pass through the MTS-LSTM, we've generated both daily and hourly predictions.\n",
    "\n",
    "If you prefer visualizations, here's what the architecture looks like:\n",
    "\n",
    "![MTS-LSTM-Visualization.jpg](https://raw.githubusercontent.com/neuralhydrology/neuralhydrology/master/examples/04-Multi-Timescale/mtslstm.jpg)\n",
    "\n",
    "You can see how the first 362 input steps are done at the daily timescale (the visualization uses 362 days, but in reality this is a tunable hyperparameter).\n",
    "Starting with day 363, two things happen:\n",
    "- The _daily_ LSTM just keeps going with daily inputs.\n",
    "- We take the hidden and cell states from day 362 and pass them through a linear layer. Starting with these new states, the _hourly_ LSTM begins processing hourly inputs.\n",
    "\n",
    "Finally, we pass the LSTMs' outputs through a linear output layer ($\\text{FC}^H$ and $\\text{FC}^D$) and get our predictions.\n",
    "\n",
    "### Some Variations\n",
    "\n",
    "Now that we have this model, we can think of a few variations:\n",
    "1. Because the MTS-LSTM has an individual branch for each timescale, we can actually use a different forcings product at each timescale (e.g., daily Daymet and hourly NLDAS). Going even further, we can use _multiple_ sets of forcings at each timescale (e.g., daily Daymet and Maurer, but only hourly NLDAS). This can improve predictions a lot (see  [Kratzert et al., 2020](https://hess.copernicus.org/articles/25/2685/2021/hess-25-2685-2021.html)).\n",
    "2. We could also use the same LSTM weights in all timescales' branches. We call this model the shared MTS-LSTM (sMTS-LSTM). In our results, the shared version generated slightly better predictions if all we have is one forcings dataset. The drawback is that the model doesn't support per-timescale forcings. Thus, if you have several forcings datasets, you'll most likely get better predictions if you use MTS-LSTM (non-shared) and leverage all your datasets.\n",
    "3. We can link the daily and hourly predictions during training to nudge the model towards predictions that are consistent across timescales. We do this by means of a regularization of the loss function that increases the loss if the average daily prediction aggregated from hourly predictions does not match the daily prediction.\n",
    "\n",
    "## Using MTS-LSTM\n",
    "\n",
    "So, let's look at some code to train and evaluate an MTS-LSTM!\n",
    "The following code uses the `neuralhydrology` package to train an MTS-LSTM on daily and hourly discharge prediction.\n",
    "For the sake of a quick example, we'll train our model on just a single basin. \n",
    "When you actually care about the quality of your predictions, you'll generally get much better model performance when training on hundreds of basins.\n",
    "\n",
    "**Note**: To be able to run this example yourself, you will need to download the [hourly NLDAS forcings and the hourly streamflow data](https://doi.org/10.5281/zenodo.4072700) and the [extended Maurer forcings](https://doi.org/10.4211/hs.17c896843cf940339c3c3496d0c1c077). Within the CAMELS root directory, place the `nldas_hourly` into a folder called `hourly` (`/path/to/CAMELS/hourly/nldas_hourly`) and the `maurer_extended` folder into `basin_mean_forcing` (`/path/to/CAMELS/basin_mean_forcing/maurer_extended`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from neuralhydrology.evaluation import metrics, get_tester\n",
    "from neuralhydrology.nh_run import start_run, eval_run\n",
    "from neuralhydrology.utils.config import Config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every experiment in `neuralhydrology` uses a configuration file that specifies its setup.\n",
    "Let's look at some of the relevant configuration options:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model:\t\t mtslstm\n",
      "use_frequencies: ['1H', '1D']\n",
      "seq_length:\t {'1D': 365, '1H': 336}\n"
     ]
    }
   ],
   "source": [
    "run_config = Config(Path(\"1_basin.yml\"))\n",
    "print('model:\\t\\t', run_config.model)\n",
    "print('use_frequencies:', run_config.use_frequencies)\n",
    "print('seq_length:\\t', run_config.seq_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`model` is obvious: We want to use the MTS-LSTM. For the sMTS-LSTM, we'd set `run_config.shared_mtslstm = True`.\n",
    "In `use_frequencies`, we specify the timescales we want to predict.\n",
    "In `seq_length`, we specify for each timescale the look-back window. Here, we'll start with 365 days look-back, and the hourly LSTM branch will get the last 14 days ($336/24 = 14$) at an hourly resolution.\n",
    "\n",
    "As we're using the MTS-LSTM (and not sMTS-LSTM), we can use different input variables at each frequency.\n",
    "Here, we use Maurer and Daymet as daily inputs, while the hourly model component uses NLDAS, Maurer, and Daymet.\n",
    "Note that even though Daymet and Maurer are daily products, we can use them to support the hourly predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dynamic_inputs:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'1D': ['prcp(mm/day)_maurer_extended',\n",
       "  'srad(W/m2)_maurer_extended',\n",
       "  'tmax(C)_maurer_extended',\n",
       "  'tmin(C)_maurer_extended',\n",
       "  'vp(Pa)_maurer_extended',\n",
       "  'prcp(mm/day)_daymet',\n",
       "  'srad(W/m2)_daymet',\n",
       "  'tmax(C)_daymet',\n",
       "  'tmin(C)_daymet',\n",
       "  'vp(Pa)_daymet'],\n",
       " '1H': ['convective_fraction_nldas_hourly',\n",
       "  'longwave_radiation_nldas_hourly',\n",
       "  'potential_energy_nldas_hourly',\n",
       "  'potential_evaporation_nldas_hourly',\n",
       "  'pressure_nldas_hourly',\n",
       "  'shortwave_radiation_nldas_hourly',\n",
       "  'specific_humidity_nldas_hourly',\n",
       "  'temperature_nldas_hourly',\n",
       "  'total_precipitation_nldas_hourly',\n",
       "  'wind_u_nldas_hourly',\n",
       "  'wind_v_nldas_hourly',\n",
       "  'prcp(mm/day)_maurer_extended',\n",
       "  'srad(W/m2)_maurer_extended',\n",
       "  'tmax(C)_maurer_extended',\n",
       "  'tmin(C)_maurer_extended',\n",
       "  'vp(Pa)_maurer_extended',\n",
       "  'prcp(mm/day)_daymet',\n",
       "  'srad(W/m2)_daymet',\n",
       "  'tmax(C)_daymet',\n",
       "  'tmin(C)_daymet',\n",
       "  'vp(Pa)_daymet']}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('dynamic_inputs:')\n",
    "run_config.dynamic_inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n",
    "\n",
    "We start model training of our single-basin toy example with `start_run`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-10-14 15:15:21,720: Logging to /home/mgauch/neuralhydrology/examples/02-Multi-Timescale/runs/test_run_1410_151521/output.log initialized.\n",
      "2020-10-14 15:15:21,721: ### Folder structure created at /home/mgauch/neuralhydrology/examples/02-Multi-Timescale/runs/test_run_1410_151521\n",
      "2020-10-14 15:15:21,721: ### Run configurations for test_run\n",
      "2020-10-14 15:15:21,722: experiment_name: test_run\n",
      "2020-10-14 15:15:21,723: use_frequencies: ['1H', '1D']\n",
      "2020-10-14 15:15:21,723: train_basin_file: 1_basin.txt\n",
      "2020-10-14 15:15:21,724: validation_basin_file: 1_basin.txt\n",
      "2020-10-14 15:15:21,724: test_basin_file: 1_basin.txt\n",
      "2020-10-14 15:15:21,725: train_start_date: 1999-10-01 00:00:00\n",
      "2020-10-14 15:15:21,725: train_end_date: 2008-09-30 00:00:00\n",
      "2020-10-14 15:15:21,726: validation_start_date: 1996-10-01 00:00:00\n",
      "2020-10-14 15:15:21,727: validation_end_date: 1999-09-30 00:00:00\n",
      "2020-10-14 15:15:21,727: test_start_date: 1989-10-01 00:00:00\n",
      "2020-10-14 15:15:21,728: test_end_date: 1996-09-30 00:00:00\n",
      "2020-10-14 15:15:21,728: device: cpu\n",
      "2020-10-14 15:15:21,729: validate_every: 5\n",
      "2020-10-14 15:15:21,729: validate_n_random_basins: 1\n",
      "2020-10-14 15:15:21,730: metrics: ['NSE']\n",
      "2020-10-14 15:15:21,731: model: mtslstm\n",
      "2020-10-14 15:15:21,731: shared_mtslstm: False\n",
      "2020-10-14 15:15:21,732: transfer_mtslstm_states: {'h': 'linear', 'c': 'linear'}\n",
      "2020-10-14 15:15:21,732: head: regression\n",
      "2020-10-14 15:15:21,733: output_activation: linear\n",
      "2020-10-14 15:15:21,733: hidden_size: 20\n",
      "2020-10-14 15:15:21,734: initial_forget_bias: 3\n",
      "2020-10-14 15:15:21,735: output_dropout: 0.4\n",
      "2020-10-14 15:15:21,735: optimizer: Adam\n",
      "2020-10-14 15:15:21,736: loss: MSE\n",
      "2020-10-14 15:15:21,736: regularization: ['tie_frequencies']\n",
      "2020-10-14 15:15:21,737: learning_rate: {0: 0.01, 30: 0.005, 40: 0.001}\n",
      "2020-10-14 15:15:21,737: batch_size: 256\n",
      "2020-10-14 15:15:21,738: epochs: 50\n",
      "2020-10-14 15:15:21,739: clip_gradient_norm: 1\n",
      "2020-10-14 15:15:21,739: predict_last_n: {'1D': 1, '1H': 24}\n",
      "2020-10-14 15:15:21,739: seq_length: {'1D': 365, '1H': 336}\n",
      "2020-10-14 15:15:21,740: num_workers: 8\n",
      "2020-10-14 15:15:21,741: log_interval: 5\n",
      "2020-10-14 15:15:21,741: log_tensorboard: False\n",
      "2020-10-14 15:15:21,742: log_n_figures: 0\n",
      "2020-10-14 15:15:21,742: save_weights_every: 1\n",
      "2020-10-14 15:15:21,743: dataset: hourly_camels_us\n",
      "2020-10-14 15:15:21,743: data_dir: /data/Hydrology/CAMELS_US\n",
      "2020-10-14 15:15:21,744: forcings: ['nldas_hourly', 'maurer_extended', 'daymet']\n",
      "2020-10-14 15:15:21,745: dynamic_inputs: {'1D': ['prcp(mm/day)_maurer_extended', 'srad(W/m2)_maurer_extended', 'tmax(C)_maurer_extended', 'tmin(C)_maurer_extended', 'vp(Pa)_maurer_extended', 'prcp(mm/day)_daymet', 'srad(W/m2)_daymet', 'tmax(C)_daymet', 'tmin(C)_daymet', 'vp(Pa)_daymet'], '1H': ['convective_fraction_nldas_hourly', 'longwave_radiation_nldas_hourly', 'potential_energy_nldas_hourly', 'potential_evaporation_nldas_hourly', 'pressure_nldas_hourly', 'shortwave_radiation_nldas_hourly', 'specific_humidity_nldas_hourly', 'temperature_nldas_hourly', 'total_precipitation_nldas_hourly', 'wind_u_nldas_hourly', 'wind_v_nldas_hourly', 'prcp(mm/day)_maurer_extended', 'srad(W/m2)_maurer_extended', 'tmax(C)_maurer_extended', 'tmin(C)_maurer_extended', 'vp(Pa)_maurer_extended', 'prcp(mm/day)_daymet', 'srad(W/m2)_daymet', 'tmax(C)_daymet', 'tmin(C)_daymet', 'vp(Pa)_daymet']}\n",
      "2020-10-14 15:15:21,745: target_variables: ['qobs_mm_per_hour']\n",
      "2020-10-14 15:15:21,746: clip_target_to_zero: ['qobs_mm_per_hour']\n",
      "2020-10-14 15:15:21,746: zero_center_target: True\n",
      "2020-10-14 15:15:21,747: number_of_basins: 1\n",
      "2020-10-14 15:15:21,747: run_dir: /home/mgauch/neuralhydrology/examples/02-Multi-Timescale/runs/test_run_1410_151521\n",
      "2020-10-14 15:15:21,748: train_dir: /home/mgauch/neuralhydrology/examples/02-Multi-Timescale/runs/test_run_1410_151521/train_data\n",
      "2020-10-14 15:15:21,748: img_log_dir: /home/mgauch/neuralhydrology/examples/02-Multi-Timescale/runs/test_run_1410_151521/img_log\n",
      "2020-10-14 15:15:21,751: ### Device cpu will be used for training\n",
      "2020-10-14 15:15:21,752: No specific hidden size for frequencies are specified. Same hidden size is used for all.\n",
      "2020-10-14 15:15:21,756: Loading basin data into xarray data set.\n",
      "100%|██████████| 1/1 [00:25<00:00, 25.58s/it]\n",
      "2020-10-14 15:15:47,382: Create lookup table and convert to pytorch tensor\n",
      "100%|██████████| 1/1 [00:04<00:00,  4.27s/it]\n",
      "# Epoch 1: 100%|██████████| 11/11 [00:08<00:00,  1.37it/s, Loss: 0.3007]\n",
      "2020-10-14 15:15:59,776: Epoch 1 average loss: 0.4808816069906408\n",
      "# Epoch 2: 100%|██████████| 11/11 [00:08<00:00,  1.34it/s, Loss: 0.3690]\n",
      "2020-10-14 15:16:08,066: Epoch 2 average loss: 0.37404750152067706\n",
      "# Epoch 3: 100%|██████████| 11/11 [00:07<00:00,  1.45it/s, Loss: 0.4100]\n",
      "2020-10-14 15:16:15,699: Epoch 3 average loss: 0.3338823630051179\n",
      "# Epoch 4: 100%|██████████| 11/11 [00:08<00:00,  1.33it/s, Loss: 0.2352]\n",
      "2020-10-14 15:16:23,980: Epoch 4 average loss: 0.2927354086529125\n",
      "# Epoch 5: 100%|██████████| 11/11 [00:08<00:00,  1.25it/s, Loss: 0.3546]\n",
      "2020-10-14 15:16:32,798: Epoch 5 average loss: 0.27082806012847205\n",
      "# Validation: 100%|██████████| 1/1 [00:27<00:00, 27.08s/it]\n",
      "2020-10-14 15:16:59,883:  -- Median validation metrics:NSE_1H: 0.43521, NSE_1D: 0.53796\n",
      "# Epoch 6: 100%|██████████| 11/11 [00:08<00:00,  1.31it/s, Loss: 0.3357]\n",
      "2020-10-14 15:17:08,299: Epoch 6 average loss: 0.24981172518296677\n",
      "# Epoch 7: 100%|██████████| 11/11 [00:08<00:00,  1.31it/s, Loss: 0.1949]\n",
      "2020-10-14 15:17:16,751: Epoch 7 average loss: 0.2203226089477539\n",
      "# Epoch 8: 100%|██████████| 11/11 [00:09<00:00,  1.18it/s, Loss: 0.1576]\n",
      "2020-10-14 15:17:26,138: Epoch 8 average loss: 0.2208765230395577\n",
      "# Epoch 9: 100%|██████████| 11/11 [00:08<00:00,  1.36it/s, Loss: 0.3064]\n",
      "2020-10-14 15:17:34,253: Epoch 9 average loss: 0.2166819613088261\n",
      "# Epoch 10: 100%|██████████| 11/11 [00:08<00:00,  1.32it/s, Loss: 0.1369]\n",
      "2020-10-14 15:17:42,605: Epoch 10 average loss: 0.204437486150048\n",
      "# Validation: 100%|██████████| 1/1 [00:01<00:00,  1.14s/it]\n",
      "2020-10-14 15:17:43,756:  -- Median validation metrics:NSE_1H: 0.51087, NSE_1D: 0.59498\n",
      "# Epoch 11: 100%|██████████| 11/11 [00:07<00:00,  1.49it/s, Loss: 0.1502]\n",
      "2020-10-14 15:17:51,160: Epoch 11 average loss: 0.19687259739095514\n",
      "# Epoch 12: 100%|██████████| 11/11 [00:07<00:00,  1.39it/s, Loss: 0.1465]\n",
      "2020-10-14 15:17:59,128: Epoch 12 average loss: 0.18620851094072516\n",
      "# Epoch 13: 100%|██████████| 11/11 [00:07<00:00,  1.40it/s, Loss: 0.2653]\n",
      "2020-10-14 15:18:06,996: Epoch 13 average loss: 0.18988782167434692\n",
      "# Epoch 14: 100%|██████████| 11/11 [00:08<00:00,  1.29it/s, Loss: 0.1523]\n",
      "2020-10-14 15:18:15,548: Epoch 14 average loss: 0.18712970614433289\n",
      "# Epoch 15: 100%|██████████| 11/11 [00:08<00:00,  1.28it/s, Loss: 0.1657]\n",
      "2020-10-14 15:18:24,187: Epoch 15 average loss: 0.17038215019486166\n",
      "# Validation: 100%|██████████| 1/1 [00:01<00:00,  1.18s/it]\n",
      "2020-10-14 15:18:25,379:  -- Median validation metrics:NSE_1H: 0.57309, NSE_1D: 0.64673\n",
      "# Epoch 16: 100%|██████████| 11/11 [00:08<00:00,  1.34it/s, Loss: 0.1787]\n",
      "2020-10-14 15:18:33,594: Epoch 16 average loss: 0.16938349604606628\n",
      "# Epoch 17: 100%|██████████| 11/11 [00:07<00:00,  1.40it/s, Loss: 0.1597]\n",
      "2020-10-14 15:18:41,518: Epoch 17 average loss: 0.16888649829409338\n",
      "# Epoch 18: 100%|██████████| 11/11 [00:07<00:00,  1.38it/s, Loss: 0.1295]\n",
      "2020-10-14 15:18:49,514: Epoch 18 average loss: 0.15878198092634027\n",
      "# Epoch 19: 100%|██████████| 11/11 [00:07<00:00,  1.38it/s, Loss: 0.1672]\n",
      "2020-10-14 15:18:57,496: Epoch 19 average loss: 0.15937954187393188\n",
      "# Epoch 20: 100%|██████████| 11/11 [00:09<00:00,  1.22it/s, Loss: 0.1608]\n",
      "2020-10-14 15:19:06,559: Epoch 20 average loss: 0.16491704095493664\n",
      "# Validation: 100%|██████████| 1/1 [00:01<00:00,  1.30s/it]\n",
      "2020-10-14 15:19:07,868:  -- Median validation metrics:NSE_1H: 0.57159, NSE_1D: 0.65612\n",
      "# Epoch 21: 100%|██████████| 11/11 [00:07<00:00,  1.44it/s, Loss: 0.0984]\n",
      "2020-10-14 15:19:15,546: Epoch 21 average loss: 0.14887346869165247\n",
      "# Epoch 22: 100%|██████████| 11/11 [00:07<00:00,  1.41it/s, Loss: 0.1066]\n",
      "2020-10-14 15:19:23,397: Epoch 22 average loss: 0.15346650982444937\n",
      "# Epoch 23: 100%|██████████| 11/11 [00:07<00:00,  1.40it/s, Loss: 0.0882]\n",
      "2020-10-14 15:19:31,273: Epoch 23 average loss: 0.15151194618506866\n",
      "# Epoch 24: 100%|██████████| 11/11 [00:07<00:00,  1.49it/s, Loss: 0.0974]\n",
      "2020-10-14 15:19:38,685: Epoch 24 average loss: 0.15529864687811246\n",
      "# Epoch 25: 100%|██████████| 11/11 [00:08<00:00,  1.37it/s, Loss: 0.1672]\n",
      "2020-10-14 15:19:46,723: Epoch 25 average loss: 0.1640351895581592\n",
      "# Validation: 100%|██████████| 1/1 [00:01<00:00,  1.02s/it]\n",
      "2020-10-14 15:19:47,752:  -- Median validation metrics:NSE_1H: 0.61226, NSE_1D: 0.68056\n",
      "# Epoch 26: 100%|██████████| 11/11 [00:07<00:00,  1.38it/s, Loss: 0.1150]\n",
      "2020-10-14 15:19:55,781: Epoch 26 average loss: 0.15235342356291684\n",
      "# Epoch 27: 100%|██████████| 11/11 [00:08<00:00,  1.32it/s, Loss: 0.1229]\n",
      "2020-10-14 15:20:04,151: Epoch 27 average loss: 0.1561715088107369\n",
      "# Epoch 28: 100%|██████████| 11/11 [00:08<00:00,  1.27it/s, Loss: 0.1593]\n",
      "2020-10-14 15:20:12,867: Epoch 28 average loss: 0.1413676691326228\n",
      "# Epoch 29: 100%|██████████| 11/11 [00:08<00:00,  1.26it/s, Loss: 0.1337]\n",
      "2020-10-14 15:20:21,597: Epoch 29 average loss: 0.14449526369571686\n",
      "2020-10-14 15:20:21,601: Setting learning rate to 0.005\n",
      "# Epoch 30: 100%|██████████| 11/11 [00:08<00:00,  1.31it/s, Loss: 0.1457]\n",
      "2020-10-14 15:20:30,025: Epoch 30 average loss: 0.14244705574078995\n",
      "# Validation: 100%|██████████| 1/1 [00:01<00:00,  1.26s/it]\n",
      "2020-10-14 15:20:31,291:  -- Median validation metrics:NSE_1H: 0.62125, NSE_1D: 0.70907\n",
      "# Epoch 31: 100%|██████████| 11/11 [00:07<00:00,  1.48it/s, Loss: 0.0679]\n",
      "2020-10-14 15:20:38,745: Epoch 31 average loss: 0.14457681571895425\n",
      "# Epoch 32: 100%|██████████| 11/11 [00:08<00:00,  1.28it/s, Loss: 0.1314]\n",
      "2020-10-14 15:20:47,367: Epoch 32 average loss: 0.1363046873699535\n",
      "# Epoch 33: 100%|██████████| 11/11 [00:07<00:00,  1.44it/s, Loss: 0.1397]\n",
      "2020-10-14 15:20:55,021: Epoch 33 average loss: 0.12838778509335083\n",
      "# Epoch 34: 100%|██████████| 11/11 [00:08<00:00,  1.34it/s, Loss: 0.1246]\n",
      "2020-10-14 15:21:03,265: Epoch 34 average loss: 0.12758956443179736\n",
      "# Epoch 35: 100%|██████████| 11/11 [00:07<00:00,  1.46it/s, Loss: 0.1570]\n",
      "2020-10-14 15:21:10,819: Epoch 35 average loss: 0.12592220645059238\n",
      "# Validation: 100%|██████████| 1/1 [00:01<00:00,  1.37s/it]\n",
      "2020-10-14 15:21:12,202:  -- Median validation metrics:NSE_1H: 0.63450, NSE_1D: 0.72433\n",
      "# Epoch 36: 100%|██████████| 11/11 [00:07<00:00,  1.41it/s, Loss: 0.1310]\n",
      "2020-10-14 15:21:20,038: Epoch 36 average loss: 0.12306197123094038\n",
      "# Epoch 37: 100%|██████████| 11/11 [00:08<00:00,  1.22it/s, Loss: 0.0963]\n",
      "2020-10-14 15:21:29,077: Epoch 37 average loss: 0.12005009637637572\n",
      "# Epoch 38: 100%|██████████| 11/11 [00:07<00:00,  1.38it/s, Loss: 0.1246]\n",
      "2020-10-14 15:21:37,097: Epoch 38 average loss: 0.12293622642755508\n",
      "# Epoch 39: 100%|██████████| 11/11 [00:08<00:00,  1.27it/s, Loss: 0.1769]\n",
      "2020-10-14 15:21:45,805: Epoch 39 average loss: 0.1347475309263576\n",
      "2020-10-14 15:21:45,809: Setting learning rate to 0.001\n",
      "# Epoch 40: 100%|██████████| 11/11 [00:07<00:00,  1.45it/s, Loss: 0.1692]\n",
      "2020-10-14 15:21:53,405: Epoch 40 average loss: 0.12377908690409227\n",
      "# Validation: 100%|██████████| 1/1 [00:01<00:00,  1.42s/it]\n",
      "2020-10-14 15:21:54,839:  -- Median validation metrics:NSE_1H: 0.63675, NSE_1D: 0.71462\n",
      "# Epoch 41: 100%|██████████| 11/11 [00:08<00:00,  1.35it/s, Loss: 0.1138]\n",
      "2020-10-14 15:22:03,006: Epoch 41 average loss: 0.11742927608164874\n",
      "# Epoch 42: 100%|██████████| 11/11 [00:07<00:00,  1.40it/s, Loss: 0.1016]\n",
      "2020-10-14 15:22:10,877: Epoch 42 average loss: 0.11336829445578835\n",
      "# Epoch 43: 100%|██████████| 11/11 [00:08<00:00,  1.27it/s, Loss: 0.1138]\n",
      "2020-10-14 15:22:19,603: Epoch 43 average loss: 0.11607782271775333\n",
      "# Epoch 44: 100%|██████████| 11/11 [00:08<00:00,  1.32it/s, Loss: 0.0893]\n",
      "2020-10-14 15:22:27,994: Epoch 44 average loss: 0.11482174491340463\n",
      "# Epoch 45: 100%|██████████| 11/11 [00:08<00:00,  1.35it/s, Loss: 0.1685]\n",
      "2020-10-14 15:22:36,184: Epoch 45 average loss: 0.12138735299760645\n",
      "# Validation: 100%|██████████| 1/1 [00:01<00:00,  1.35s/it]\n",
      "2020-10-14 15:22:37,537:  -- Median validation metrics:NSE_1H: 0.64254, NSE_1D: 0.71223\n",
      "# Epoch 46: 100%|██████████| 11/11 [00:07<00:00,  1.51it/s, Loss: 0.1058]\n",
      "2020-10-14 15:22:44,857: Epoch 46 average loss: 0.12129346416755156\n",
      "# Epoch 47: 100%|██████████| 11/11 [00:07<00:00,  1.46it/s, Loss: 0.1647]\n",
      "2020-10-14 15:22:52,417: Epoch 47 average loss: 0.11692171882499348\n",
      "# Epoch 48: 100%|██████████| 11/11 [00:08<00:00,  1.22it/s, Loss: 0.0811]\n",
      "2020-10-14 15:23:01,443: Epoch 48 average loss: 0.11074774576859041\n",
      "# Epoch 49: 100%|██████████| 11/11 [00:07<00:00,  1.45it/s, Loss: 0.1226]\n",
      "2020-10-14 15:23:09,050: Epoch 49 average loss: 0.1074042638594454\n",
      "# Epoch 50: 100%|██████████| 11/11 [00:09<00:00,  1.19it/s, Loss: 0.1224]\n",
      "2020-10-14 15:23:18,313: Epoch 50 average loss: 0.11328567022627051\n",
      "# Validation: 100%|██████████| 1/1 [00:01<00:00,  1.31s/it]\n",
      "2020-10-14 15:23:19,637:  -- Median validation metrics:NSE_1H: 0.64218, NSE_1D: 0.72372\n"
     ]
    }
   ],
   "source": [
    "start_run(config_file=Path(\"1_basin.yml\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation\n",
    "\n",
    "Given the trained model, we can generate and evaluate its predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-10-14 15:23:46,372: No specific hidden size for frequencies are specified. Same hidden size is used for all.\n",
      "2020-10-14 15:23:46,378: Using the model weights from runs/test_run_1410_151521/model_epoch050.pt\n",
      "# Evaluation: 100%|██████████| 1/1 [00:17<00:00, 17.86s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys(['01022500'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_dir = Path(\"runs/test_run_1410_151521\")  # you'll find this path in the output of the training above.\n",
    "\n",
    "# create a tester instance and start evaluation\n",
    "tester = get_tester(cfg=run_config, run_dir=run_dir, period=\"test\", init_model=True)\n",
    "results = tester.evaluate(save_results=False, metrics=run_config.metrics)\n",
    "\n",
    "results.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a closer look at the predictions and do some plots, starting with the daily results.\n",
    "Note that units are mm/h even for daily values, since we predict daily averages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Daily metrics:\n",
      "  NSE: 0.768\n",
      "  MSE: 0.003\n",
      "  RMSE: 0.052\n",
      "  KGE: 0.742\n",
      "  Alpha-NSE: 0.770\n",
      "  Beta-NSE: 0.011\n",
      "  Pearson-r: 0.884\n",
      "  FHV: -23.069\n",
      "  FMS: -20.862\n",
      "  FLV: 54.632\n",
      "  Peak-Timing: 0.500\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract observations and simulations\n",
    "daily_qobs = results[\"01022500\"][\"1D\"][\"xr\"][\"qobs_mm_per_hour_obs\"]\n",
    "daily_qsim = results[\"01022500\"][\"1D\"][\"xr\"][\"qobs_mm_per_hour_sim\"]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.plot(daily_qobs[\"date\"], daily_qobs, label=\"Observed\")\n",
    "ax.plot(daily_qsim[\"date\"], daily_qsim, label=\"Simulated\")\n",
    "ax.legend()\n",
    "ax.set_ylabel(\"Discharge (mm/h)\")\n",
    "ax.set_title(f\"Test period - daily NSE {results['01022500']['1D']['NSE_1D']:.3f}\")\n",
    "\n",
    "# Calculate some metrics\n",
    "values = metrics.calculate_all_metrics(daily_qobs.isel(time_step=-1), daily_qsim.isel(time_step=-1))\n",
    "print(\"Daily metrics:\")\n",
    "for key, val in values.items():\n",
    "    print(f\"  {key}: {val:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...and finally, let's look more closely at the last few months' hourly predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract a date slice of observations and simulations\n",
    "hourly_xr = results[\"01022500\"][\"1H\"][\"xr\"].sel(date=slice(\"10-1995\", None))\n",
    "\n",
    "# The hourly data is indexed with two indices: The date (in days) and the time_step (the hour within that day).\n",
    "# As we want to get a continuous plot of several days' hours, we select all 24 hours of each day and then stack\n",
    "# the two dimensions into one consecutive datetime dimension.\n",
    "hourly_xr = hourly_xr.isel(time_step=slice(-24, None)).stack(datetime=['date', 'time_step'])\n",
    "hourly_xr['datetime'] = hourly_xr.coords['date'] + hourly_xr.coords['time_step']\n",
    "\n",
    "hourly_qobs = hourly_xr[\"qobs_mm_per_hour_obs\"]\n",
    "hourly_qsim = hourly_xr[\"qobs_mm_per_hour_sim\"]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.plot(hourly_qobs[\"datetime\"], hourly_qobs, label=\"Observation\")\n",
    "ax.plot(hourly_qsim[\"datetime\"], hourly_qsim, label=\"Simulation\")\n",
    "ax.set_ylabel(\"Discharge (mm/h)\")\n",
    "ax.set_title(f\"Test period - hourly NSE {results['01022500']['1H']['NSE_1H']:.3f}\")\n",
    "_ = ax.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
